Computed tomography uses X-ray projections from multiple vantage points to construct a three dimensional (3D) data set. This process results in the generation of a “CT” image. Each point in the 3D data set describes the X-ray attenuating properties of a corresponding point in an object in space. CT scanners are used in medicine, material diagnostics, and airport security.
Metals are highly attenuating to X-rays and, because of this, they produce artifacts in reconstructed images. As a result, efforts have been made to eliminate these artifacts, however, all of the approaches developed to date suffer from various disadvantages, e.g., computational overhead, image accuracy, etc.
A. Context
Computed tomography devices have a source of X-rays which are sent through an object of interest to a set of detectors. Each detector measures the amount of X-ray attenuation which occurs along the path connecting the source to that detector. This attenuation data is sometimes known as projection data, inasmuch as the detected X-ray intensities are representative of the object's shadow projected on to the surfaces of the detectors. Many sets of projection data, taken from different angles of view, can be used to mathematically reconstruct the full 3D shape and attenuating properties of the object. The mathematical transformation known as “backprojection” solves the essentially geometrical problem of determining the shape and composition of the object that would produce the observed X-ray projection data.
FIG. 1 illustrates some common components and concepts in CT scanning. Typically an X-ray source 1 emits X-rays 2, some of which pass through an object 4, and are detected by an array of X-ray detectors 5. The X-ray source 1 and detectors 5 are typically mounted on a rotating gantry 6A which rotates in a direction 6 about a center point 3, which allows for the measurement of X-ray attenuation through the object from multiple orientations, whereby to generate the projection data which is used to reconstruct the full 3D shape and attenuating properties of object 4.
X-rays are reduced in intensity exponentially with passing distance through an object, and accounting for this allows one to interpret X-ray signals from a geometric perspective. Equation 1 below is the equation describing the exponential decay of X-rays as they pass through some material, where I is the X-ray intensity after having passed through some material, I0 is the unimpeded X-ray intensity, x is the amount of material that the X-rays have passed through, and mu is a constant for a given material and X-ray energy:I(x)=I0e(−x mu)  Eqn 1
In the instance where the X-rays used to probe the object of interest are of a single energy (color), Equation 1 is easily inverted:X=−ln(I/I0)/mu  Eqn 2
Equation 2 implies that, assuming that mu is a constant, one can determine the geometric length of the material that the X-rays have passed through by knowing I and I0. Unfortunately, both the material characteristics (mu) and the length of the path through the object may vary with orientation. The approximate solution to this problem is known as backprojection. X-ray detector data can be backprojected to produce an approximate image of the object, where each point in the image is a measure of the X-ray attenuation of a corresponding point in the object.
B. Exemplary Mechanisms which Create Metal Artifacts
For context, two mechanisms will now be described which lead to the creation of artifacts within reconstructed images. These two mechanisms are saturation and beam hardening.
1. Saturated Attenuation
The following simplified example, using a CT imaging system with a single detector, illustrates how the mechanism of signal saturation can impact images (actual CT systems use arrays of multiple detectors to faithfully reconstruct objects of complex shapes located anywhere within the designed field of view, however, the following example using a single detector can adequately demonstrate the saturation concept).
FIG. 2 shows the location of an X-ray source 9 and a single detector 7 as the source and detector are rotated about the object of interest through a series of angular positions 1-9 (source positions are denoted with an “s” and detector positions are denoted with a “d”). Further incremental source positions s10-s18 and detector positions d10-d18 (not shown) would mirror the data acquired over source positions s1-s9 and detector positions d1-d9.
The amount of signal detected at each rotational position is related to the length of material which the X-ray passes through, as described by Equation 1 above. In FIG. 3, the blue dots show the X-ray signal as a function of angular position. Notice that in FIG. 2, the X-ray path associated with source position 2 (s2) or 3 (s3) passes through a small amount of material, whereas the X-ray path of source position 7 (s7) passes through the longest extent of the object. This effect is mirrored in FIG. 3, where the minimum X-ray attenuation is seen at source position 3 (s3), and the maximum X-ray attenuation is seen at source position 7 (s7).
Metals are highly attenuating to X-rays. While an X-ray detector is, ideally, capable of detecting smaller and smaller quantities of X-rays, detector “noise” and photon quantization place practical physical limits on detector sensitivity. To illustrate the consequences of this, consider the previous example, but place a limit on the lowest possible detectable signal. In FIG. 3, the “x's” represent a truncated version of the data, which mimics the impact of limited detection at low X-ray fluxes. Note how the saturated signal in FIG. 3 produces significantly lower measured X-ray attenuation at angular positions 6-8 (and at angular positions 15-17) than the ideal signal would produce. Real-world systems generally have a continuous roll-off of detectability at low X-ray fluxes, but for illustrative purposes, it can be modelled as an abrupt truncation such as is shown in FIG. 3. FIG. 4 is a “prediction” of the shape of an object which is consistent with the model-truncated data of FIG. 3 (i.e., if the model-truncated data of FIG. 3 were to be backprojected to form an image, the object shown in FIG. 4 would be the result).
In practice, high attenuation levels (e.g., such as those produced when scanning objects which comprise metals), correlated with small signal levels, result in detector data which are effectively truncated to some smallest value. Geometrically, in the simplified example discussed above, this translates to a long, fixed path length for the X-ray paths which are highly attenuated (e.g., by metal which is present in the scan field). In a complete system with many detectors, this manifests itself with streaks which align themselves with the longest X-ray paths through dense objects.
2. Beam Hardening
Within the CT industry, X-ray sources are typically “spectrally broad”. X-ray photons of all energies are emitted simultaneously and the average degree of X-ray attenuation (mu) varies with the amount of material the X-rays have passed through. However, within the broad X-ray spectrum of the source, X-rays of lower energy are attenuated more quickly (higher mu) than X-rays of higher energy (lower mu). This problem of higher attenuation of lower energy X-rays is known as “beam hardening”.
Moderate beam hardening can manifest itself as a slight reduction in the reconstructed CT values at the center of an object (a “dishing” effect). More extreme beam hardening effects can cause image artifacts more like the saturation artifacts described above. By way of example, FIG. 5 shows the effects of more extreme beam hardening. Objects comprising metals generally produce more extreme beam hardening effects due to their high attenuation of lower energy X-rays.
A number of ways have been developed for addressing beam hardening.
The most direct method is to use a monoenergetic X-ray source which produces a single X-ray energy. Data obtained with monoenergetic sources are largely immune to the effects of beam hardening. However, this approach is expensive and hence impractical for most applications.
Another method is to assume that the mu values of the materials are very close to that of water and to develop an equation (similar to Equation 1) which describes the relationship between distance and X-ray intensity for a polyenergetic X-ray source. This approach works fairly well in medical CT where most biological tissue has X-ray attenuation similar to that of water. However, this approach does not work well where the object comprises metal. More particularly, a simple beam hardening correction which assumes that all mu values are close to that of water will not accurately correct for beam hardening where there is metal present in the scan field and will lead to streaks within the image.
Other approaches for correcting for beam hardening may perform better in the presence of metal, but these often involve multiple reconstructions (backprojections) and/or data modelling (forward projections), and can be computationally expensive.
Recently, another method has been developed which involves the use of two different polyenergetic X-ray sources. The two sources have different polyenergetic spectra, one generally being higher energy than the other. These two polyenergetic spectra can be processed together so as to produce synthetic monoenergetic data and, ultimately, synthetic monoenergetic images (see U.S. Patent Application Publication No. US 2017/0023498 A1 filed by Photo Diagnostic Systems, Inc. and William A. Worstell et al. for METHOD AND APPARATUS FOR PERFORMING MULTI-ENERGY (INCLUDING DUAL ENERGY) COMPUTED TOMOGRAPHY (CT) IMAGING, which patent application is hereby incorporated herein by reference, which provides a description of how two different polyenergetic X-ray sources may be used to produce synthetic monoenergetic images). While such synthetic monoenergetic images are more resistant to the effects of beam hardening, the synthetic monoenergetic data derived from multiple broad-spectrum measurements are approximate and must assume a range of typical material attenuation responses. Hence, synthetic monoenergetic images generated using two different polyenergetic X-ray sources still do not perfectly correct for the beam hardening effects of metals.
C. Metal Artifact Correction
Described below are some common approaches for correcting scan data which has been negatively impacted by the presence of metal in the scan field.
A first class of metal artifact correction involves the identification and replacement of metal-contaminated data in the raw data space (i.e., in the fanogram, sinogram or projection space). The simplest approaches involve replacing data regions that have been contaminated by metal with an interpolation of the data from neighboring uncontaminated detector channels (see Willi Kalender, Robert Hebel, Johannes Ebersberger, “Reduction of CT Artifacts Caused by Metallic Implants”, Radiology, vol 164, no. 2, pp 576 (1987) and Gary Glover, Norbert Pelc, “An algorithm for the reduction of metal clip artifacts in CT reconstructions”, Med. Phys., vol 8, no. 6, pp 799-807 (1981)). Because of the simplicity of this approach, this “sinogram completion” method has been highly studied and evaluated. These interpolation methods have been identified as effective for a narrow range of cases where the metal in question is embedded within a homogeneous setting. For example, metal staples within the abdomen. These interpolation methods fail when the adjacent detector channels, used for the interpolation, have data values which are different from the adjacent tissue, e.g., where a staple is near bone and soft tissue. In addition, these schemes generally rely on simple linear or polynomial interpolations which are not guaranteed to generate values which are consistent with a physical object. In general, this class of metal artifact corrections reduces some artifacts, but often generates new artifacts.
A second class of metal artifact corrections involves the use of “priors” for replacing sinogram values. More particularly, with this approach, regions of the image containing metal are identified, replaced with a moderated value, and then forward projected back to the detector space. These new detector values are then used to replace the metal-contaminated detector values. This ensures that the replacement data is self-consistent with a real object in image space, and eliminates some of the artifacts generated when using a simple interpolation scheme. For medical applications, metal artifacts within the image can safely be limited to values associated with either water or bone, and this a-priori knowledge is then utilized in the medical CT artifact corrections. The use of priors in security applications poses a greater challenge because of the wider range of materials encountered. Further refinements of this method include additional filtering and interpolation steps (Gary Glover, Norbert Pelc, “An algorithm for the reduction of metal clip artifacts in CT reconstructions”, Med. Phys., vol 8, no. 6, pp 799-807 (1981) and K. Y. Jeong and J. B. Ra, “Reduction of artifacts due to multiple metallic objects in computed tomography”, Proc. SPIE, vol 7258, p. 72583E (2009)). This class of metal artifact corrections is still limited in its efficacy and can be computationally expensive since this class of metal artifact corrections involves a forward projection of a complete image.
A third class of metal artifact corrections is to rely on an iterative reconstruction process where many cycles of forward and back projections are used to derive the most likely image which is consistent with the measured data. This approach is one of the most effective means for reducing metal artifacts, but is often complex and computationally expensive.